Gene/Protein Disease Symptom Drug Enzyme Compound
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 30,880 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

This paper describes Phase I clinical studies of Antineoplaston A2 injections. The studies involved 15 patients diagnosed with advanced neoplastic diseases including cancers of the breast, bladder, lung, kidney, oesophagus, colon and liver, mesothelioma and glioma. Antineoplaston A2 was administered in divided doses daily intravenously through a subclavian vein catheter. The treatment was given from 53 to 358 days. The highest dosage administered was 147 mg/kg/24 h. Only minimal adverse effects were noticed sometime during the treatment, including fever, chills and myalgia. Desirable side-effects included increase of platelet and white blood cell counts, hypertrophy of epidermis and decrease of cholesterol and triglyceride levels. Nine patients showed objective response to the treatment. Cases of complete remission included adenocarcinoma of the lung, mesothelioma, metastatic liver and bladder cancers. In an additional case of breast cancer, the patient obtained complete remission of liver metastasis and stabilization of bone metastases. Partial remission was accomplished in cancers of the breast and oesophagus. Three patients, including cases of adenocarcinoma of the lung, mesothelioma and bladder cancer, were in complete remission for over five years.
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PMID:Initial clinical study with antineoplaston A2 injections in cancer patients with five years' follow-up. 356 10

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).
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PMID:Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA. 3160 16

The Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) is among long non-coding RNAs (lncRNAs) which has disapproved the old term of "junk DNA" which was used for majority of human genome which are not transcribed to proteins. An extensive portion of literature points to the fundamental role of this lncRNA in tumorigenesis process of diverse cancers ranging from solid tumors to leukemia. Being firstly identified in lung cancer, it has prognostic and diagnostic values in several cancer types. Consistent with the proposed oncogenic roles for this lncRNA, most of studies have shown up-regulation of MALAT1 in malignant tissues compared with non-malignant/normal tissues of the same source. However, few studies have shown down-regulation of MALAT1 in breast cancer, endometrial cancer, colorectal cancer and glioma. In the current study, we have conducted a comprehensive literature search and provided an up-date on the role of MALAT1 in cancer biology. Our investigation underscores a potential role as a diagnostic/prognostic marker and a putative therapeutic target for MALAT1.
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PMID:Metastasis Associated Lung Adenocarcinoma Transcript 1: An update on expression pattern and functions in carcinogenesis. 3171 17

Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and is the most lethal cancer of all urologic cancers. We developed an unsupervised deep learning method, stacked denoising autoencoders (SdA), by integrating multi-platform genomic data for subtyping ccRCC with the goal of assisting diagnosis, personalized treatments and prognosis. We successfully found two subtypes of ccRCC using five genomics datasets for Kidney Renal Clear Cell Carcinoma (KIRC) from The Cancer Genome Atlas (TCGA). Correlation analysis between the last reconstructed input and the original input data showed that all the five types of genomic data positively contribute to the identification of the subtypes. The first subtype of patients had significantly lower survival probability, higher grade on neoplasm histology and higher stage on pathology than the other subtype of patients. Furthermore, we identified a set of genes, proteins and miRNAs that were differential expressed (DE) between the two subtypes. The function annotation of the DE genes from pathway analysis matches the clinical features. Importantly, we applied the model learned from KIRC as a pre-trained model to two independent datasets from TCGA, Lung Adenocarcinoma (LUAD) dataset and Low Grade Glioma (LGG), and the model stratified the LUAD and LGG patients into clinical associated subtypes. The successful application of our method to independent groups of patients supports that the SdA method and the model learned from KIRC are effective on subtyping cancer patients and most likely can be used on other similar tasks. We supplied the source code and the models to assist similar studies at https://github.com/tjgu/cancer_subtyping.
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PMID:Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders. 3185 23